Marginal Structural Cox Models with Case-Cohort Sampling

• Activity Number: 620
• Type: Invited
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Hana Lee*+ and Michael G. Hudgens and Jianwen Cai and Stephen R. Cole
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: Case-cohort Study ; Causal Inference ; Cox Models ; Marginal Structural Models ; Survival Analysis

Abstract

An objective of biomedical cohort studies often entails assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. In this talk we consider estimation of parameters in the semiparametric marginal structural Cox model (Cox MSM) from a case-cohort study. Case-cohort sampling entails assembling covariate histories only for cases and a random subcohort, which can be cost effective, particularly in large cohort studies with low incidence rates. Following Cole et al. (2012), we consider estimating the causal hazard ratio from a Cox MSM by maximizing a weighted-pseudo-partial-likelihood. The estimator is shown to be consistent and asymptotically normal under certain regularity assumptions.